{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UKVSQH3SUIG3FMYK7OX7W2KFD6","short_pith_number":"pith:UKVSQH3S","schema_version":"1.0","canonical_sha256":"a2ab281f72a20db2b30afbaffb69451f8f314d6c0fd9591c0fedfa5c01751a10","source":{"kind":"arxiv","id":"2409.04481","version":1},"attestation_state":"computed","paper":{"title":"Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-bio.QM","authors_text":"Geoffrey I. Webb, George Church, Huan Yee Koh, Lauren T. May, Li Li, Maddie Yang, Shirui Pan, Yizhen Zheng","submitted_at":"2024-09-06T02:03:38Z","abstract_excerpt":"The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.04481","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2024-09-06T02:03:38Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"16f6f9f91b6ac4463006c3bdd178d7570978c291f0a390685c5d317cc92a802d","abstract_canon_sha256":"b07409db236928e8d58e6aa873e7e6c76351bf69eb665554d43301a2f0fbbecf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:12.736357Z","signature_b64":"R+LZK02k1J2bjavR3z96km4svZPUjM3g52f3M1ygXFOWtJNdci3O/9LP6UYkOxkDQIrVR7Ny8f44c8CKU5tCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2ab281f72a20db2b30afbaffb69451f8f314d6c0fd9591c0fedfa5c01751a10","last_reissued_at":"2026-07-05T09:04:12.735909Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:12.735909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-bio.QM","authors_text":"Geoffrey I. Webb, George Church, Huan Yee Koh, Lauren T. May, Li Li, Maddie Yang, Shirui Pan, Yizhen Zheng","submitted_at":"2024-09-06T02:03:38Z","abstract_excerpt":"The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.04481","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.04481/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.04481","created_at":"2026-07-05T09:04:12.735965+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.04481v1","created_at":"2026-07-05T09:04:12.735965+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.04481","created_at":"2026-07-05T09:04:12.735965+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKVSQH3SUIG3","created_at":"2026-07-05T09:04:12.735965+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKVSQH3SUIG3FMYK","created_at":"2026-07-05T09:04:12.735965+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKVSQH3S","created_at":"2026-07-05T09:04:12.735965+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25250","citing_title":"LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2509.11295","citing_title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","ref_index":148,"is_internal_anchor":false},{"citing_arxiv_id":"2509.11295","citing_title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","ref_index":149,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6","json":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6.json","graph_json":"https://pith.science/api/pith-number/UKVSQH3SUIG3FMYK7OX7W2KFD6/graph.json","events_json":"https://pith.science/api/pith-number/UKVSQH3SUIG3FMYK7OX7W2KFD6/events.json","paper":"https://pith.science/paper/UKVSQH3S"},"agent_actions":{"view_html":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6","download_json":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6.json","view_paper":"https://pith.science/paper/UKVSQH3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.04481&json=true","fetch_graph":"https://pith.science/api/pith-number/UKVSQH3SUIG3FMYK7OX7W2KFD6/graph.json","fetch_events":"https://pith.science/api/pith-number/UKVSQH3SUIG3FMYK7OX7W2KFD6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6/action/storage_attestation","attest_author":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6/action/author_attestation","sign_citation":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6/action/citation_signature","submit_replication":"https://pith.science/pith/UKVSQH3SUIG3FMYK7OX7W2KFD6/action/replication_record"}},"created_at":"2026-07-05T09:04:12.735965+00:00","updated_at":"2026-07-05T09:04:12.735965+00:00"}